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Remote Sensing Application in Sustainable Urban Planning and Environmental Services in the Big Data Era

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 32614

Special Issue Editor

Prof. Dr. Danlin Yu

E-Mail Website
Guest Editor
Department of Earth and Environmental Studies, Montclair State University, Montclair, NJ 07043, USA
Interests: urban remote sensing; geographic information science; system dynamics; complex system simulation; regional development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportunities to examine the dynamics of urban landscapes. Urban scholars now are equipped with abundant data to examine many theoretical arguments that often resulted from limited and indirect observations and less-than-ideal controlled experiments as manifested from only surveys or statistical yearbooks.

In the meantime, the rapid development of telecommunications and mobile technology and the emergence of online search engines and social media platforms in the past decade has fundamentally changed human activities and the urban landscape. The availability of abundant real-time, geotagged individual pieces of information has drastically changed how scholars see the dynamic urban landscape—for the first time from both a micro and macro level. The seemingly chaotic micro pieces of individual activities are now able to be assembled to macro patterns in almost real time, exhibiting the constantly moving, changing, and evolving urban landscape to urban scholars. Yet, patterns also emerge from observing this dynamic landscape that remain to be explored using newly developed tools and approaches.

The combination of these two types of data sources results in explosive and mind-blowing discoveries in contemporary urban studies, especially for the purposes of sustainable urban planning and development and urban health. Urban scholars are now, for the first time, able to model, simulate, and predict changes in the urban landscape using real-time data to produce the most realistic modeling results, which provides invaluable information for urban planners and governments to aim for a sustainable and healthy urban future. This Special Issue attempts to assemble a cohort of studies that specifically examines how to incorporate the most up-to-date remote sensing data sources and geotagged social media/search engine data to support sustainable urban planning and development and to promote urban health in this new era.

Submissions covering the following areas are specifically encouraged:

  • Urban simulations supported by remote sensing and big data
  • Mechanisms of urban landscape change
  • Spatiotemporal examination of urban landscape
  • Noval analytical approaches utilizing remote sensing and big data in urban studies
  • Studies of urban vibrancy with remote sensing and big data analytical approaches
  • Integrating RS and big data to investigate healthy and sustainable urban development
  • Investigating urban environmental services via urban remote sensing and big data

Prof. Dr. Danlin Yu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data described urban landscape
  • urban health
  • urban remote sensing
  • sustainable urban development
  • spatiotemporal data analysis in urban studies
  • remote sensing and big data supported urban simulation
  • urban vibrancy

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Related Special Issue

Published Papers (8 papers)

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Research

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26 pages, 1935 KiB  
Article
Bridging the Gap: Analyzing the Relationship between Environmental Justice Awareness on Twitter and Socio-Environmental Factors Using Remote Sensing and Big Data
by Charles Knoble and Danlin Yu
Remote Sens. 2023, 15(23), 5510; https://doi.org/10.3390/rs15235510 - 27 Nov 2023
Cited by 4 | Viewed by 2137
Abstract
Mounting awareness of the discriminatory distribution of environmental factors has increasingly placed environmental justice at the forefront of discussions on sustainable development, but responses to these disparities are often too little, too late. Remote sensing has emerged as a potential solution to this [...] Read more.
Mounting awareness of the discriminatory distribution of environmental factors has increasingly placed environmental justice at the forefront of discussions on sustainable development, but responses to these disparities are often too little, too late. Remote sensing has emerged as a potential solution to this problem, capitalizing on the ability to capture high-resolution, spatially explicit data in near-real time. However, a conventional reliance on physical measurements and surface-level analyses risks overlooking the experiences and perceptions of affected communities. It is against this backdrop that the potential integration of remote sensing imagery and socially sensed big data such as social media data assumes a novel and promising role. This study aims to discern the feasibility, opportunities, and implications of integrating the spatial insights provided by remote sensing with the experiential narratives shared on social media platforms, bridging the gap between objective environmental data and community-driven perspectives. We explore this subject in two ways, analyzing the geographic relationship between environmental justice Tweets and environmental justice factors, and reviewing Tweets produced during an extensive wildfire. Remote sensing indexes for green and blue space were reviewed and tested, selecting the measures of best fit to act as independent variables alongside traditional environmental justice factors in the broader analysis. Results from regression models indicate a negative relationship between the number of Tweets utilizing environmental justice relevant terms and the presence of ecosystem services as captured by an NDMI, suggesting a broad awareness of injustice and a relationship between remote sensing and social media. However, there is simultaneously a negative relationship between socially vulnerable populations and Tweets with environmental justice words. This suggests that generally, there is discussion on Twitter about injustice when resources are not present, but the voices of vulnerable populations are often less visible, either as a result of urban bias or a lack of concern for injustices due to habitual ignorance. Our study demonstrates the potential for integrating remote sensing imagery and social sensing data to play a substantial role in detecting injustices and corroborating data collected through community science initiatives. Full article
(This article belongs to the Special Issue Remote Sensing Application in Sustainable Urban Planning and Environmental Services in the Big Data Era)
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23 pages, 5553 KiB  
Article
Urban Vulnerability Analysis Based on Micro-Geographic Unit with Multi-Source Data—Case Study in Urumqi, Xinjiang, China
by Jianghua Zheng, Danlin Yu, Chuqiao Han and Zhe Wang
Remote Sens. 2023, 15(16), 3944; https://doi.org/10.3390/rs15163944 - 9 Aug 2023
Viewed by 1843
Abstract
This study introduces a novel approach to urban public safety analysis inspired by a streetscape analysis commonly applied in urban criminology, leveraging the concept of micro-geographical units to account for urban spatial heterogeneity. Recognizing the intrinsic uniformity within these smaller, distinct environments of [...] Read more.
This study introduces a novel approach to urban public safety analysis inspired by a streetscape analysis commonly applied in urban criminology, leveraging the concept of micro-geographical units to account for urban spatial heterogeneity. Recognizing the intrinsic uniformity within these smaller, distinct environments of a city, the methodology represents a shift from large-scale regional studies to a more localized and precise exploration of urban vulnerability. The research objectives focus on three key aspects: first, establishing a framework for identifying and dividing cities into micro-geographical units; second, determining the type and nature of data that effectively illustrate the potential vulnerability of these units; and third, developing a robust and reliable evaluation index system for urban vulnerability. We apply this innovative method to Urumqi’s Tianshan District in Xinjiang, China, resulting in the formation of 30 distinct micro-geographical units. Using WorldView-2 remote sensing imagery and the object-oriented classification method, we extract and evaluate features related to vehicles, roads, buildings, and vegetation for each unit. This information feeds into the construction of a comprehensive index, used to assess public security vulnerability at a granular level. The findings from our study reveal a wide spectrum of vulnerability levels across the 30 units. Notably, units X1 (Er Dao Bridge) and X7 (Gold Coin Mountain International Plaza) showed high vulnerability due to factors such as a lack of green spaces, poor urban planning, dense building development, and traffic issues. Our research validates the utility and effectiveness of the micro-geographical unit concept in assessing urban vulnerability, thereby introducing a new paradigm in urban safety studies. This micro-geographical approach, combined with a multi-source data strategy involving high-resolution remote sensing and field survey data, offers a robust and comprehensive tool for urban vulnerability assessment. Moreover, the urban vulnerability evaluation index developed through this study provides a promising model for future urban safety research across different cities. Full article
(This article belongs to the Special Issue Remote Sensing Application in Sustainable Urban Planning and Environmental Services in the Big Data Era)
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30 pages, 10032 KiB  
Article
Exploring the Relationships between Land Surface Temperature and Its Influencing Factors Using Multisource Spatial Big Data: A Case Study in Beijing, China
by Xiaoxi Wang, Yaojun Zhang and Danlin Yu
Remote Sens. 2023, 15(7), 1783; https://doi.org/10.3390/rs15071783 - 27 Mar 2023
Cited by 13 | Viewed by 3472
Abstract
A better understanding of the relationship between land surface temperature (LST) and its influencing factors is important to the livable, healthy, and sustainable development of cities. In this study, we focused on the potential effect of human daily activities on LST from a [...] Read more.
A better understanding of the relationship between land surface temperature (LST) and its influencing factors is important to the livable, healthy, and sustainable development of cities. In this study, we focused on the potential effect of human daily activities on LST from a short-term perspective. Beijing was selected as a case city, and Weibo check-in data were employed to measure the intensity of human daily activities. MODIS data were analyzed and used for urban LST measurement. We adopted spatial autocorrelation analysis, Pearson correlation analysis, and spatial autoregressive model to explore the influence mechanism of LST, and the study was performed at both the pixel scale and subdistrict scale. The results show that there is a significant and positive spatial autocorrelation between LSTs, and urban landscape components are strong explainers of LST. A significant and positive effect of human daily activities on LST is captured at night, and this effect can last and accumulate over a few hours. The variables of land use functions and building forms show varying impacts on LST from daytime to nighttime. Moreover, the comparison between results at different scales indicates that the relationships between LST and some explainers are sensitive to the study scale. The current study enriches the literature on LST and offers meaningful and practical suggestions for the monitoring, early warning, and management of urban thermal environment with remote sensing technology and spatial big data sources. Full article
(This article belongs to the Special Issue Remote Sensing Application in Sustainable Urban Planning and Environmental Services in the Big Data Era)
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19 pages, 16037 KiB  
Article
Urban Area Characterization and Structure Analysis: A Combined Data-Driven Approach by Remote Sensing Information and Spatial–Temporal Wireless Data
by Xiangyu Chen, Kaisa Zhang, Gang Chuai, Weidong Gao, Zhiwei Si, Yijian Hou and Xuewen Liu
Remote Sens. 2023, 15(4), 1041; https://doi.org/10.3390/rs15041041 - 14 Feb 2023
Cited by 4 | Viewed by 2297
Abstract
Analysis of urban area function is crucial for urban development. Urban area function features can help to conduct better urban planning and transportation planning. With development of urbanization, urban area function becomes complex. In order to accurately extract function features, researchers have proposed [...] Read more.
Analysis of urban area function is crucial for urban development. Urban area function features can help to conduct better urban planning and transportation planning. With development of urbanization, urban area function becomes complex. In order to accurately extract function features, researchers have proposed multisource data mining methods that combine urban remote sensing and other data. Therefore, the research of efficient multisource data analysis tools has become a new hot topic. In this paper, a novel urban data analysis method combining spatiotemporal wireless network data and remote sensing data was proposed. First, a Voronoi-diagram-based method was used to divide the urban remote sensing images into zones. Second, we combined period and trend components of wireless network traffic data to mine urban function structure. Third, for multisource supported urban simulation, we designed a novel spatiotemporal city computing method combining graph attention network (GAT) and gated recurrent unit (GRU) to analyze spatiotemporal urban data. The final results prove that our method performs better than other commonly used methods. In addition, we calculated the commuting index of each zone by wireless network data. Combined with the urban simulation conducted in this paper, the dynamic changes of urban area features can be sensed in advance for a better sustainable urban development. Full article
(This article belongs to the Special Issue Remote Sensing Application in Sustainable Urban Planning and Environmental Services in the Big Data Era)
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20 pages, 4080 KiB  
Article
Measuring Urban Poverty Spatial by Remote Sensing and Social Sensing Data: A Fine-Scale Empirical Study from Zhengzhou
by Kun Wang, Lijun Zhang, Meng Cai, Lingbo Liu, Hao Wu and Zhenghong Peng
Remote Sens. 2023, 15(2), 381; https://doi.org/10.3390/rs15020381 - 8 Jan 2023
Cited by 10 | Viewed by 3449
Abstract
Urban poverty is a major obstacle to the healthy development of urbanization. Identifying and mapping urban poverty is of great significance to sustainable urban development. Traditional data and methods cannot measure urban poverty at a fine scale. Besides, existing studies often ignore the [...] Read more.
Urban poverty is a major obstacle to the healthy development of urbanization. Identifying and mapping urban poverty is of great significance to sustainable urban development. Traditional data and methods cannot measure urban poverty at a fine scale. Besides, existing studies often ignore the impact of the built environment and fail to consider the equal importance of poverty indicators. The emerging multi-source big data provide new opportunities for accurately measuring and monitoring urban poverty. This study aims to map urban poverty spatial at a fine scale by using multi-source big data, including social sensing and remote sensing data. The urban core of Zhengzhou is selected as the study area. The characteristics of the community’s living environment are quantified by accessibility, block vitality, per unit rent, public service infrastructure, and socio-economic factors. The urban poverty spatial index (SI) model is constructed by using the multiplier index of the factors. The SOM clustering method is employed to identify urban poverty space based on the developed SI. The performance of the proposed SI model is evaluated at the neighborhood scale. The results show that the urban poverty spatial measurement method based on multi-source big data can capture spatial patterns of typical urban poverty with relatively high accuracy. Compared with the urban poverty space measured based on remote sensing data, it considers the built environment and socio-economic factors in the identification of the inner city poverty space, and avoids being affected by the texture information of the physical surface of the residential area and the external structure of the buildings. Overall, this study can provide a comprehensive, cost-effective, and efficient method for the refined management of urban poverty space and the improvement of built environment quality. Full article
(This article belongs to the Special Issue Remote Sensing Application in Sustainable Urban Planning and Environmental Services in the Big Data Era)
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21 pages, 3895 KiB  
Article
A Multi-Indicator Evaluation Method for Spatial Distribution of Urban Emergency Shelters
by Xinxiang Wang, Minglei Guan, Chunlai Dong, Jingzhe Wang, Yong Fan, Fei Xin and Guoyun Lian
Remote Sens. 2022, 14(18), 4649; https://doi.org/10.3390/rs14184649 - 17 Sep 2022
Cited by 7 | Viewed by 2140
Abstract
Evaluation of the spatial distribution of urban emergency shelters can effectively identify defects in the current distribution of urban emergency shelters and weaknesses in the overall evacuation service capacity of the city and provide reference for improving the level of urban emergency shelters [...] Read more.
Evaluation of the spatial distribution of urban emergency shelters can effectively identify defects in the current distribution of urban emergency shelters and weaknesses in the overall evacuation service capacity of the city and provide reference for improving the level of urban emergency shelters and evacuation and disaster relief capacity. At present, evaluation of the spatial distribution of urban emergency shelters is mainly carried out on three aspects: effectiveness, accessibility, and safety. However, there are problems, such as individual evaluation scales and incomplete indicator systems, unreasonable allocation of indicator weights, and ignoring the influence of fuzzy incompatibility between different indicator attributes on the evaluation results. In this paper, we start from two scales, the individual emergency shelter and the regional groups of emergency shelters. Based on the five criteria of effectiveness, accessibility, safety, suitability, and fairness, the evaluation indicator system of the spatial distribution of urban emergency shelters was constructed. It was combined with AHP, CRITIC, the optimal weight coefficient solution method based on the maximum deviation sum of squares theory, and fuzzy optimization theory to construct a multi-indicator evaluation model. Further, the spatial distribution condition of the existing emergency shelter in Shanghai was evaluated. The results show that: among the existing ninety-one emergency shelters in Shanghai, there are nine places with unreasonable spatial distribution; nineteen places are comparatively unreasonable. From the scale of regional groups, there is one district (Pudong New District) with unreasonable spatial distribution: its relative superiority value is far lower than other districts, and there are three districts that are comparatively unreasonable. Further, the evaluation scores of the spatial distribution reasonableness of emergency shelters in each region of Shanghai show a high–low–middle distribution from the downtown area of Shanghai outward. The evaluation indicator system and evaluation method used in this paper can effectively reflect the deficiencies in the spatial distribution of urban emergency shelters, thus providing a reference for the relevant departments to improve and plan emergency shelters. Full article
(This article belongs to the Special Issue Remote Sensing Application in Sustainable Urban Planning and Environmental Services in the Big Data Era)
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20 pages, 7326 KiB  
Article
Identification and Evaluation of the Polycentric Urban Structure: An Empirical Analysis Based on Multi-Source Big Data Fusion
by Yuquan Zhou, Xiong He and Yiting Zhu
Remote Sens. 2022, 14(11), 2705; https://doi.org/10.3390/rs14112705 - 4 Jun 2022
Cited by 18 | Viewed by 3628
Abstract
Identifying and evaluating polycentric urban spatial structure is essential for understanding and optimizing current urban development. In order to accurately identify the urban centers of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this study firstly fused nighttime light data, POI data, and population [...] Read more.
Identifying and evaluating polycentric urban spatial structure is essential for understanding and optimizing current urban development. In order to accurately identify the urban centers of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this study firstly fused nighttime light data, POI data, and population migration data based on wavelet transform, then identified the polycentric spatial structure of the GBA by carrying out cluster and outlier analysis, and evaluated the level of different urban centers byconducting geographical weighted regression analysis. Using data fusion, we identified 4579.81 km² of the urban poly-center area in the GBA, with an identification accuracy of 93.22%. Although the number and spatial extent of the identified urban poly-centers are consistent with the GBA development plan outline, the poly-center level evaluation results are inconsistent with the development plan, which shows there are great differences in actual development levels among different cities in the GBA. By identifying and grading the polycentric spatial structure of the GBA, this study accurately analyzed the current spatial distribution and could provide policy implications for the GBA’s future development and planning. Full article
(This article belongs to the Special Issue Remote Sensing Application in Sustainable Urban Planning and Environmental Services in the Big Data Era)
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Review

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34 pages, 1407 KiB  
Review
Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent Decades
by Danlin Yu and Chuanglin Fang
Remote Sens. 2023, 15(5), 1307; https://doi.org/10.3390/rs15051307 - 26 Feb 2023
Cited by 42 | Viewed by 11659
Abstract
During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportunities to examine the dynamics of urban landscapes. In the meantime, the [...] Read more.
During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportunities to examine the dynamics of urban landscapes. In the meantime, the rapid development of telecommunications and mobile technology, alongside the emergence of online search engines and social media platforms with geotagging technology, has fundamentally changed how human activities and the urban landscape are recorded and depicted. The combination of these two types of data sources results in explosive and mind-blowing discoveries in contemporary urban studies, especially for the purposes of sustainable urban planning and development. Urban scholars are now equipped with abundant data to examine many theoretical arguments that often result from limited and indirect observations and less-than-ideal controlled experiments. For the first time, urban scholars can model, simulate, and predict changes in the urban landscape using real-time data to produce the most realistic results, providing invaluable information for urban planners and governments to aim for a sustainable and healthy urban future. This current study reviews the development, current status, and future trajectory of urban studies facilitated by the advancement of remote sensing and spatial big data analytical technologies. The review attempts to serve as a bridge between the growing “big data” and modern urban study communities. Full article
(This article belongs to the Special Issue Remote Sensing Application in Sustainable Urban Planning and Environmental Services in the Big Data Era)
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